Stephen Hutt Artificial Intelligence | Learning Analytics | Big Data
All conference proceedings listed are strictly peer reviewed, * Denotes Preprint Draft

2021

Annual Meeting of the Cognitive Science Society [CogSci]

July

Who’s stopping you? - using microanalysis to explore the impact of science anxiety on self-regulated learning operations

S. Hutt, J. Ocumpaugh, J. M. A. L. Andres, A. Munshi, N. Bosch, R. S. Baker, Y. Zhang, L. Paquette, S. Slater, and G. Biswas,

International Conference on Educational Data Mining [EDM]

July

Sharpest tool in the shed: Investigating smart models of self-regulation and their impact on learning

S. Hutt, J. Ocumpaugh, J. M. A. L. Andres, N. Bosch, L. Paquette, G. Biswas, and R. S. Baker

International Conference on Artificial Intelligence in Education [AIED]

June

Affect targeted interviews for understanding student frustration

R. S. Baker, N. Nasiar, J. L. Ocumpaugh, S. Hutt, J. M. A. L. Andres, S. Slater, M. Schofield, A. Moore, L. Paquette, A. Munshi, and G. Biswas

International Conference on Artificial Intelligence in Education [AIED]

June

A comparison of hints vs. scaffolding in a mooc with adult learners

Y. Zhou, J. Andres-Bray, S. Hutt, K. Ostrow, and R. S. Baker

International Conference on Artificial Intelligence in Education [AIED]

June

Towards Sharing Student Models Across Learning Systems

R. S. Baker, B. McLaren, S. Hutt, J. Richey, E. Rowe, M. Almeda, M. Mogessie, and J. M. A. L. Andres

Conference on Human Factors in Computing Systems [CHI]

May

Breaking out of the Lab: Mitigating Mind Wandering with Gaze-Based Attention-Aware Technology in Classrooms

S. Hutt, K. Krasich, J. R. Brockmole, and S. K. D’Mello

International Conference on Learning Analytics and Knowledge [LAK]

April

What You Do Predicts How You Do, Prospectively Modeling Student Quiz Performance Using Activity Features in an Online Learning Environment

E. Jensen, T. Umada, N. C. Hunkins, S. Hutt , A. C. Huggins-Manley and S. K. D'Mello

2020

Dissertation

July

Scaling Up: Moving Automated Gaze-Based Engagement Out of the Lab

S. Hutt

Journal of Research on Adolesence [JRA]

March

How does high school extracurricular participation predict bachelor’s degree attainment? it’s complicated

M. Gardner, S. Hutt, A. L. Duckworth, and S. K. D’Mello

2019

User Modeling and User-Adapted Interaction [UMUAI]

June

Automated gaze-based mind wandering detection during computerized learning in classrooms

S. Hutt, K. Krasich, C. Mills, N. Bosch, S. White, J. R. Brockmole, and S. K. D’Mello

American Educational Research Journal [AERJ]

May

Why high school grades are better predictors of on-time college graduation than are admissions test scores: The role of self- regulation and cognitive ability.

B. M. Galla, E. P. Shulman, B. Plummer, M. Gardner, S. Hutt, J. Goyer, A. Finn, S. K. D’Mello, and A.L. Duckworth

International Conference on Educational Data Mining [EDM]

Montreal, Canada - July

Evaluating fairness and generalizability in models predicting on-time graduation from college applications

S. Hutt, M. Gardner, A. L. Duckworth, and S. K. D’Mello

International Conference on Educational Data Mining [EDM]

Montreal, Canada - July

Generalizability of sensor-free affect detection models in a longitudinal dataset of tens of thousands of students

E. Jensen, S. Hutt, and S. K. D’Mello

Conference on Human Factors in Computing Systems [CHI]

Glasgow, Scotland UK - May

Time to scale: Generalizable affect detection for tens of thousands of students across an entire school year

S. Hutt , J. F. Grafsgaard, and S. K. D’Mello

International Conference on Learning Analytics & Knowledge [LAK]

Tempe, AZ, USA - March

Language as thought: Using natural language processing to model noncognitive traits that predict college success

C. Stone, A. Quirk, M. Gardener, S. Hutt, A. L. Duckworth, and S. K. D’Mello

2018

Journal of Experimental Psychology

Gaze-based signatures of mind wandering during real-world scene processing

K. Krasich, R. McManus, S. Hutt, M. Faber, S. K. D’Mello, and J. R. Brockmole

International Conference on Artificial Intelligence in Education [AIED]

London, UK - June

MindTS: Testing a brief mindfulness intervention with an intelligent tutoring system

K. Krasich, S. Hutt, C. Mills, C. A. Spann, J. R. Brockmole, and S. K. D’Mello

International Conference on Learning Analytics and Knowledge [LAK]

Sydney, NSW, Australia - March

Prospectively predicting 4-year college graduation from student applications

S. Hutt, M. Gardener, D. Kamentz, A. L. Duckworth, and S. K. D’Mello

2017

IEEE Frontiers in Education Conference [FIE]

Indianapolis, IN, USA - October

Placating plato with plates of pasta: An interactive tool for teaching the dining philosophers problem

J. DeBenedetto, S. Hutt, L. Fause, A. Liu, and N. Kremer-Herman

Conference on User Modeling, Adaptation and Personalization [UMAP]

Bratislava, Slovakia - July

Out of the fr-eye-ing pan: Towards gaze-based models of attention during learning with technology in the classroom

S. Hutt, C. Mills, N. Bosch, K. Krasich, J. R. Brockmole, and S. K. D’Mello

International Conference on Educational Data Mining [EDM]

Wuhan, China - July

Gaze-based detection of mind wandering during lecture viewing

S. Hutt, J. Hardey, R. Bixler, A. Stewart, E. Risko, and S. K. D’Mello

2016

International Conference on Educational Data Mining [EDM]

Raleigh, NC, USA - July

The Eyes Have It: Gaze-based Detection of Mind Wandering during Learning with an Intelligent Tutoring System

S. Hutt, C. Mills, S. White, P. J. Donnelly, and S. K. D’Mello